Estimating geolocation of a user terminal

ABSTRACT

A system and method for estimating a geolocation. The method includes tessellating a coverage area into a camping cell and adjacent cells; subdividing the camping cell into grid points where each grid point of the grid points has an associated relative offset from a camping beam center; illuminating, with a platform, the camping cell with a camping beam and each of the adjacent cells with adjacent beams; receiving a camping beam signal strength and adjacent beams signal strengths for each grid point of the grid points; profiling, at each grid point of the grid points, ratios of the camping beam signal strength to each one of the adjacent beams signal strengths; mapping the ratios and the associated relative offset of each grid point of the grid points; and estimating a relative geolocation of a User Terminal (UT) from the camping beam center based on a UT camping beam signal strength and UT adjacent beams signal strengths.

CROSS-REFERENCE TO RELATED APPLICATIONS AND INCORPORATION BY REFERENCE

The present application is a continuation of U.S. application Ser. No.17/072,938, filed Oct. 16, 2020, which is incorporated herein byreference in its entirety.

FIELD

A system and method for estimating a geolocation of a User Terminal (UT)by estimating a UT's geolocation, based on power ratios of a camping andadjacent beam strength, when the UT is disposed within a beam's coveragearea of a multibeam communication system. The system and method may beused when the beams are formed from a High-Altitude Platform (HAP),Geosynchronous Earth Orbit (GEO) satellite, a Medium Earth Orbit (MEO),a Low Earth Orbit (LEO) satellite, an airplane, a platform 20,000 feetabove sea-level or the like. The present teachings may use interpolationor a neural network to estimate a geolocation that substitutes for orcomplements a Global Navigation Satellite System (GNSS).

BACKGROUND

The Prior art satellite systems, for example, Geosynchronous EarthOrbiting (GEO) systems, usually implement User Terminal (UT) positioningwith the help of a Global Navigation Satellite System (GNSS) such as aGlobal Positioning Satellite System (GPS). In some instances, GNSS maybe unavailable due to intended or unintended jamming or interference. Assuch, it is desirable that a UT estimate its location by itself or incooperation with a gateway with some precision.

Common prior art for positioning techniques uses these four parameters:Angle of Arrival, Time of arrival, time difference of arrival andreceived signal strength indicator. In the context of UT positioning ina satellite network, angle of arrival typically does not providesufficient resolution to be useful. The other parameters are typicallyused to derive range information. The UT location is determined bytriangulation with known positions. Common prior art positioningtechniques are generally known as range-based positioning techniquesthat use a trilateration or multi-lateration technique to compute thelocation of UT. All prior techniques require multiple signal sources,each from a known location.

A good example is the Global Positioning Satellite System (GPS). GPS isbased on Time Difference of arrival. It requires at least three plus oneMedium Earth Orbiting (MEO) satellites to accurately locate an object.In geosynchronous satellite (GEO) communication systems, the UTs areusually pointed to a serving satellite using directional antennas toestablish good connectivity with the satellite and it is impractical tohave access to more than one GEO satellite to implement themulti-lateration technique.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that is further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe Error! Hyperlink reference not valid.used to limit the scope of theclaimed subject matter.

There is need to identify UT location in GEO satellite system in case ofcountry boundary related handover, legal inception and billing policy.There is no good prior art about User Terminal (UT) location estimationin without GNSS's help. The prior art is particularly lacking insatellite systems.

The present teachings measure a UT's relative location from a beamcenter when the UT is disposed within a beam's coverage area. The UT'sgeolocation may be calculated by offsetting a beam center geolocationlocation with the UT's location relative to the beam center. A NeuralNetwork classifier may be used (for example, at the UT or the gateway(GW)) to estimate a UT's relative location to the beam center.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect includes a method for estimating a geolocation. Themethod includes tessellating a coverage area into a camping cell andadjacent cells; subdividing the camping cell into grid points where eachgrid point of the grid points has an associated relative offset from acamping beam center; illuminating, with a platform, the camping cellwith a camping beam and each of the adjacent cells with adjacent beams;receiving a camping beam signal strength and adjacent beams signalstrengths for each grid point of the grid points; profiling, at eachgrid point of the grid points, ratios of the camping beam signalstrength to each one of the adjacent beams signal strengths; mapping theratios and the associated relative offset of each grid point of the gridpoints; and estimating a relative geolocation of a User Terminal (UT)from the camping beam center based on a UT camping beam signal strengthand UT adjacent beams signal strengths. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the mapping may include populating a look up table (LUT)with ratios and the associated relative offset, and the estimating byinterpolation from measured ratios of the UT camping beam signalstrength and the UT adjacent beams signal strengths between the ratiosof grid points in the LUT to find a best match, and determines therelative geolocation of the UT based on the best match. The populatingmay include adding the UT geolocation, the UT camping beam signalstrength and UT adjacent beams signal strengths to the LUT.

In some embodiments, the mapping may include pretraining neural networkweights of a neural network with ratios and the associated relativeoffset, and the estimating estimates the relative geolocation of the UTwith the neural network. The method may include receiving a UTgeolocation, a UT camping beam signal strength and UT adjacent beamssignal strengths; and training the neural network with the UTgeolocation, the UT camping beam signal strength and UT adjacent beamssignal strengths.

The receiving may include measuring, at one or more of the grid points,the camping beams signal strength and the adjacent beams signalstrengths. The receiving may include computing, at one or more of thegrid points, the camping beams signal strength and the adjacent beamssignal strengths. The method may include adjusting the camping beamsignal strength and each one of the adjacent beams signal strengths to areference transmit power. The ratios are calculated as Pc/Pai or10*log10(Pc/Pai), with the Pc set to the camping beam signal strengthand the Pai set to each one of the adjacent beams signal strengths inturn. The method may include predicting a location estimate offset basedon a slow varying geosynchronous-earth orbit satellite pointing error ora movement of the camping beam center based on feedback from a pilot UTand ephemeris of the platform; and compensating for the locationestimate offset in the estimating. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

One general aspect includes a system to estimate a geolocation. Thesystem includes a coverage area tessellated into a camping cell andadjacent cells, and the camping cell subdivided into grid points, whereeach grid point of the grid points has an associated relative offsetfrom a center of the camping cell; a platform to illuminate the campingcell with a camping beam and each of the adjacent cells with adjacentbeams; and a geolocation estimator, The geolocation estimator mayreceive a camping beam signal strength and adjacent beams signalstrengths for each grid point of the grid points, may profile, at eachgrid point of the grid points, ratios of the camping beam signalstrength to each one of the adjacent beams signal strengths, may map theratios and the associated relative offset of each grid point of the gridpoints, and may estimate a relative geolocation of a user terminal (UT)from the camping beam center based on a UT camping beam signal strengthand UT adjacent beams signal strengths. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Additional features will be set forth in the description that follows,and in part will be apparent from the description, or may be learned bypractice of what is described.

DRAWINGS

In order to describe the way, the above-recited and other advantages andfeatures may be obtained, a more particular description is providedbelow and will be rendered by reference to specific embodiments thereofwhich are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments and are not, therefore, to belimiting of its scope, implementations will be described and explainedwith additional specificity and detail with the accompanying drawings.

FIG. 1 illustrates a multi-beam satellite or high-altitude platformsystem according to various embodiments.

FIG. 2 illustrates a camping cell subdivided into grid points accordingto various embodiments.

FIG. 3 illustrates power ratios of exemplary grid points according tovarious embodiments.

FIG. 4 illustrates a method for estimating a geolocation according tovarious embodiments.

FIG. 5 illustrates a method for interpolating a geolocation according tovarious embodiments.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The present teachings may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as SMALLTALK, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that afeature, structure, characteristic, and so forth described in connectionwith the embodiment is included in at least one embodiment of thepresent invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

There is no good prior art about User Terminal (UT) location estimationwithout GNSS's help. The prior art is particularly lacking in satellitesystems. The UT location can be very useful in satellite communication.Often, satellite beams are wide so that a beam may illuminate a campingcell and neighboring cells. As such a beam may cover severaljurisdictions such as countries, states/provinces, counties or the like.The UT's geolocation information may be used for billing, lawenforcement and other purposes. A signal power difference measurementmapping may be used to estimate a geolocation of a user terminal (UT) ina multi-beam satellite or high-altitude platform system. The mapping maybe used as weights for a Neural Network based classifier, forinterpolation based on a look up table, or the like. The camping beamrefers to the beam serving the UT. Camping cell refers to the cell areaserved by the camping beam within which the UT is located. Unlessspecified otherwise, a camping cell and camping beam are interchangeablein the present teachings.

For mobile satellite applications, the UT location may be used forasynchronous communication from a gateway to the UT, for example, whenthe UT is being paged, when the UT is in IDLE mode. Without the UT'slocation, paging messages may have to be duplicated and broadcast inmultiple beams, reducing spectrum efficiency. With the knowledge of UT'slocation, a gateway (GW) may map paging messages to a specific beam.Thus, a paging message needs only be sent to the beam where UT resides.The UT location can also be useful for a handover process.

The present teachings use a UT's measurement downlink reference signalpower from the adjacent cells, {Pa1, Pa2, . . . , Pak}, where k is thetotal number of adjacent cells. In a typical hexagon cellular networkstructure k is 6. In some embodiments, a measurement of the downlinksignal power (Pc) for the cell that the UT is camping on may be used.The UT may report the measurements back to the gateway (GW). The GW maythen estimate the UT location based on a difference between thereference signal power of the camped cell and adjacent cells referenceeither in linear ratio or in a decibel (dB) scale (DPi) which areconceptually equivalent. In other words, in linear ratio DPi=Pc/Pai orin decibel DPi=10*log10(Pc/Pai), where i=1,2, . . . ,J, where J is thetotal number of adjacent beams.

The estimate may be based on the downlink signal power differences DPimeasured at the UT. The downlink signal power differences DPi may becommunicated to the neural network classifier may be disposed in the GWor the UT. The GW may receive the downlink signal power differences DPifrom the UT via a communications link, for example, a satellite link.

As a relative power, DPi is not affected by fading caused signal powerfluctuation. As such the relative power DPi may be used during trainingor post training operation.

The Neural Network weights are trained based on the known beam patternof the cell which UT is camped on and its adjacent cells. The weightscan be updated during operation with the help of geolocation informationfor a pilot UT disposed in the coverage area of the beam serving the UT.Pilot UTs collect and disseminate training data in training oroperation.

FIG. 1 illustrates a multi-beam satellite or high-altitude platformsystem according to various embodiments.

A beam communication system 100 may include a gateway 116 connecting toa platform 110 to provide a radio signal over a coverage area. Thecoverage area may be tessellated into a camping cell 105 and adjacentcells 102-1, 102-2, 102-3, 102-4, 102-5, 102-6. The tessellation mayresult in irregular shapes in the coverage area due to coverage areacontour, angle of beam, shaping of the beam by a beamformer or the like.The shape of the camping cell 105 is not primary information needed forthe present teachings; the beam center geolocation and relative offsetsof each of the grid points from the beam center (FIG. 1 center C) arethe primary information needed for the present teachings.

The gateway 116 may transmit uplinks 120 to the platform 110. Theplatform 110 may relay the uplinks 120 to corresponding cells as beamsor downlinks. In FIG. 1 , a camping beam 112 (selected from the uplinks120) may illuminate the camping cell 105. Camping cell 105 may bemanaged as a hexagon 104 having a radius 106 within the system 100. Thecamping beam 112 may target a center C. The center C of camping cell 105and a beam center of the camping beam 112 may or may not be coincident.In some embodiments, relative offsets of the grid points are measuredfrom the beam center of the camping beam 112. An adjacent beam 114 mayilluminate the adjacent cell 102-3. Even though all the adjacent cells102-1, 102-2, 102-3, 102-4, 102-5, 102-6 are illuminated bycorresponding beams, only one of the adjacent beams (adjacent beam 114)is illustrated in FIG. 1 for clarity.

The system 100 may include a User Terminal (UT) 108 disposed within thecamping cell 105. The UT 108 may receive the camping beam 112 and theadjacent beams 114. The UT 108 may estimate and/or measure a signalstrength of the camping beam 112 and the adjacent beams 114. The signalstrength may be estimated/measured in decibels. The UT 108 may receivethe camping beam 112 and the adjacent beams 114. The UT 108 maycommunicate the signal strengths to a geolocation estimator 118.

The system 100 may include a geolocation estimator 118. In someembodiments, the geolocation estimator 118 may be disposed with thegateway 116. In some embodiments, the geolocation estimator 118 may bedisposed with the UT 108. In some embodiments, the beams (the campingbeam 112 and the adjacent beams 114) may be transmitted using one ormore different transmit powers. The signal strength may be normalized bybaselining the transmit power of a beam to a standard/common strength.The normalizing maybe performed by the geolocation estimator 118. Thegeolocation estimator 118 may calculate geolocations relative to thecamping beam center C of the camping beam 112. The geolocation of thecamping beam center C may be known by the gateway 116. In someembodiments, the geolocation estimator may include a neural networkclassifier. In some embodiments, the geolocation estimator may include ageolocation interpolator including a look up table.

The platform 110 may include a High-Altitude Platform (HAP),Geosynchronous Earth Orbit (GEO) satellite, a Medium Earth Orbit (MEO),a Low Earth Orbit (LEO) satellite, an airplane, a platform about 20,000feet above sea-level or the like. For multi-beam non-geosynchronoussatellite networks, signal strength measurements from the same satelliteat a given time may be used. For non-GEO satellite systems, the offsetcalculation to determine the UT geolocation may be based on anon-geosynchronous satellite's ephemeris and a geolocation of the beamcenter over time. These values be used to calculate the geolocation ofthe UT.

A beam's signal power ratio profile of a specific location in a cell isunique for each grid point. The beam may be a forward link from thegateway 116 to the platform 110 to the camping cell 102-C. At a uniquelocation inside a cell, the ratio of forward link signal power (Pc) ofthe center cell over that of the adjacent cells (Pai, which is from theith adjacent cell) provides a power ratio profile Pc/Pai. In someembodiments, only a first ring of the adjacent cells is evaluated toestimate a geolocation.

The frequency reuse factor of the beam communication system is not afactor as the signal power of the adjacent cells provides theinformation necessary to estimate the geolocation.

FIG. 2 illustrates a camping cell subdivided into grid points accordingto various embodiments.

FIG. 2 illustrates a camping cell 200 subdivided into grid points, forexample, 443 grid points. At every grid point in the camping cell,ratios (see FIG. 3 ) of a radiation pattern from the camping beam andadjacent beams (for example, the six adjacent beams of FIG. 1 ) can beused to train Neural Network (NN) weights assuming the transmissionpower of the cells is equal or can be stored as LUT for patternmatching. Different grid point will have a different profile pattern,for example, a DPi profile pattern, and a different pattern may bemapped to a different grid point. In some embodiments, the grid pointsmay be normalized to a triple radius cross point, for example, tripleradius cross point 122. A grid point geolocation may be associated witheach grid point. The grid point geolocation may be relative to thecamping cell center.

FIG. 3 illustrates power ratios of exemplary grid points according tovarious embodiments.

In the multi beam system, each cell has a reference signal, and all thereference signals are orthogonal to each other, and a signal power ofthe reference signal can be measured. Each cell's reference signal(generally a sidelobe of a reference signal) may be measured withinadjacent cells. So, for example, the UT 108 can measure a signalstrength of the reference signal of each of the adjacent cells. Thereference signal of each of the adjacent cells does not need to be ofsufficient power to obtain a signal lock; it just needs to be measured.When the transmission power of the cells is unequal, the transmissionpower can be adjusted according to a reference transmit power. Thereference transmit power may be used in training a neural network orother reference mappings. Ratios of camping cell beam power to anadjacent cell beam for grid points 202, 204, 206, 208, 210 and 212 areplotted in FIG. 3 .

A Closer Look of Power Ratio Profile vs Grid Points

In some embodiments, the coordinates of grid points are normalized to acamping cells triple crossover radius (for example, radius 106 of FIG. 1). An adjacent cell's frequency reuse factor doesn't affect the adjacentcell signal (for example, forward link) power measurement. The UT needonly tune its RF front end to an adjacent cell's frequency band.

A unique PN sequence (Pseudo-random Noise sequence) may be assigned to acell as a cell reference signal for forward link transmission from GW toUT. The PN sequences may be orthogonal to each other for co-channelcells. Exemplary widely used PN sequences are Gold Code Sequences. Eachcell may broadcast it's the PN sequence periodically or continuouslybased on system need.

In some embodiments, signal transmission power and power density areidentical for different beams (for example forward links) even thoughthey use different frequency band and different polarization. The signalpower at the receiver may rely on the radiation pattern of the beams. Insome embodiments, the transmission power may vary between differentbeams, and the variation in the power can be compensated aftercalibration and possibly normalization.

Neural Network Training

In some embodiments, known beam patterns and signal strengths at variousgrid points in a camping cell may be used for Neural Network Training.In some embodiments, signal strengths for a subset of grid points may beused for the Neural network training. In some embodiments, signalstrengths of some of the grid points may be calculated or estimated. Insome embodiments, signal strengths of some of the grid points may bemeasured, for example, by a pilot UT.

Training data input for a neural network may be simulated using a beampattern in a far field using algorithms known in the art. In someembodiments, training data may provide for the triple crossover powerlevel may be 5 dB lower than beam peak for beam patterns of each cell.Moreover, in the training data, all the coordinates of grid points of acell may be normalized to its triple crossover radius.

In some embodiments, a difference (expressed as DPi) between the campingcell reference signal power and adjacent cell reference signal power maybe expressed as DPi=10*log10(Pc/Pai), where an adjacent cell's forwardlink reference signal power is Pai (i=1,2, . . . ,6) and the campingcell's forward link signal power is Pc. The DPi at different grid pointsmay be used as training data input. When fading happens at UT, both Pcand Pai may experience path loss or degradation, and DPi may remain thesame. As such, the DPi may be chosen to train the neural network ratherthan the Pc and Pai.

A Neural Network Model for grid point coordination estimation mayassume:

-   6 input nodes for DPi;-   2 output nodes for soft normalized grid point coordinates;-   2 hidden layers;-   20 nodes for each layer;-   Weights trained on DPi inside a cell with 6 adjacent cells; and-   half of the total grid points are used for training.

The estimated location error is proportional to the cell size. If thecell is bigger, the absolute estimation error is bigger. If a cell sizeof 100 km in radius, the location estimation error is dependent on C/Imeasurement error. In simulations, a location estimation tested withGaussian distributed measurement error was evaluated on all the gridpoints. The estimated geolocations provided by a 20-node neural networkresulted in geolocations were:

-   less than 1.2 km with 90% confidence for perfect signal measurement,-   less than 1.75 km with 90% confidence for 1 sigma 0.2 dB measurement    error,-   less than 4 km with 90% confidence for 1 sigma 0.5 dB measurement    error, and-   less than 7.5 km with 90% confidence for 1 sigma 1 dB measurement    error.-   Better results were achieved with a 35-node neural network that    estimated geolocations with 0.65 km with 90% confidence. In some    embodiments, number of nodes may be increased to 35-nodes when an    accuracy less than 1 km is desirable.

Measurement Error Contributor and Potential Improvement

Some factors may contribute to the UT receiver signal power measurementerror for the power ratio profile. For example, a slow varying satellitepointing error may cause a location estimation offset that can becompensated by a pilot UT's common shift in location estimation or evenprediction. Pilot UTs are terminals are provided an accurate location ofself, which may be either manually input by user/installer or GNSSbased.

A fast-moving satellite, for example, a MEO or LEO satellite, pointingerror will affect location estimation accuracy. Location estimationaccuracy may be improved by accounting for the satellite location and acamping beam center geolocation more frequently. The accuracy may beimproved by computing the results in shorter time interval and/or usingthe history of previous locations data. Independent measurements bypilot UTs could be used to reduce the errors quite a bit as the pilot UTprobably did not move between the measurements.

Errors may also be introduced by a mismatch in a training radiationpattern and an in-field radiation pattern. A good calibration proceduremay reduce the mismatch.

The training can be done based on the beam pattern provided by themanufacturer initially and can be updated by actual measurement.Retraining of the neural network may be attempted in-field by pilot UTs.The pilot UTs can solve the mismatch problem if UTs with known accuratelocations or GPS receivers provide location reference and long-termmeasurement of the UT's power ratio profile.

Thermal noise's effect will be trivial with long enough averaging periodwhen Pc/Noise and Pai/Noise is high enough (e.g. 20 dB or above)

UT Location Estimation Method

FIG. 4 illustrates a method for estimating a geolocation according tovarious embodiments.

A method 400 for estimating a geolocation of a user terminal (UT) mayinclude an operation 402 to tessellate a satellite coverage area into acamping cell and adjacent cells. The method 400 may include operation404 to subdivide the camping cell into grid points, each grid pointhaving an associated relative offset from a camping beam center. Themethod 400 may include operation 406 to illuminate the camping cell witha camping beam aimed at the camping beam center and each of the adjacentcells with adjacent beams. The method 400 may include operation 408 toreceive a camping beam signal strength and adjacent beams signalstrengths for each grid point. The method 400 may include operation 410for profiling, at each grid point, a signal power ratio of the campingbeam signal strength against each one of the adjacent beams signalstrengths.

In some embodiments, the method 400 may include operation 412 andoperation 414. Operation 412 may map the signal power ratios and theassociated relative offset of each grid point as NN weights. Theoperation 414 may estimate, with the neural network, a relativegeolocation of a UT from the center based on a UT camping beam signalstrength and UT adjacent beams signal strengths. In other embodiments,the method 400 may include operation 412′ and operation 414′. Operation412′ may map the signal power ratios and the associated relative offsetof each grid point as NN by populating a Look-Up Table (LUT). Theoperation 414′ may estimate a relative geolocation of a UT from thecenter based on a UT camping beam signal strength and UT adjacent beamssignal strengths by interpolating for the relative geolocation acrossthe LUT.

In exemplary embodiments, the method 400 for UT location estimation isassisted by a gateway. The UT may measure signal power of the campingcell and adjacent (usually 7 cells; camping cell on a coverage areaborder may have less than adjacent cells) and reports the measurementsto the gateway. Neural networks weights used at the gateway may bepretrained with the beam patterns before network operation. In otherembodiments, neural network weights may be built during networkoperation as, for example, pilot UTs report their location and signalmeasurements. The data from the pilot UTs may be used to train theneural network weights and continuously optimize/train the weights. A UTwithout location information only report power measurement, GW cancalculate its location based on the trained NN weights.

The gateway may have a set of neural network weights associated witheach cell served by the gateway. As such, a gateway serving N-cells of acoverage area may have N-sets of neural network weights. Each of theN-cells may have different shapes and sizes.

UT Location Estimation by Interpolation

FIG. 5 illustrates a method for interpolating a geolocation according tovarious embodiments.

A method 500 for interpolating a geolocation may include an operation502 to populate a Look Up Table (LUT) with DPi as vectors. The LUT mayinclude a DPi value for every cell in a coverage area. In someembodiments, operation 502 may, for each grid point (m,n) in the campingcell, save one signal power ratio profile as a vector DPi(m,n,i=1:J) inthe look up table (LUT) for the grid points, using for example, a gridpoint step of x_step and y_step to step between different cells includedin the coverage area.

The method 500 may include operation 504 to estimate a geolocation basedon a signal power ratio profile vector, for example, vector DPi′(i=1:J),for a camping beam. The method 500 may include an operation 506 to set asearch range equal to the grid points in the camping cell. The method500 may include an operation 507 to find, in the search range, theclosest grid point and the corresponding offset (m0,n0), for example, bysearching for min(Euclidean Distance (DP(m,n,i=1:J)−DP′(i=1:J)).

The method 500 may include operation 510 to determine if the vector atcorresponding offset (m0,n0) is within a threshold, min(EuclideanDistance (DP(m,n,i=1:J)−DP′(i=1:J))<ε. Operation 510 may also include(not shown) a loop counter that stops the searching after a thresholdnumber of iterations. When the determination at operation 510 indicateswithin threshold, method 500 may output the grid point (m0,n0) atoperation 512. When the determination at operation 510 indicates notwithin threshold, method 500 may include operation 514 to add finer gridpoints using interpolation around (m0,n0) to add finer grid points, forexample, eight finer grid points using m0+/−0.5*sx_step,n0+/−0.5*sy_step. After adding the finer grid points at operation 514,the method 500 may perform operation 516 to set search range to thenewly interpolated grid points plus (m0, n0) and reperform operations508, 510, 514 and 516 until operation 510 indicates otherwise.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artconsidering the above teachings. It is therefore to be understood thatchanges may be made in the embodiments disclosed which are within thescope of the invention as outlined by the appended claims. Having thusdescribed aspects of the invention, with the details and particularityrequired by the patent laws, what is claimed and desired protected byLetters Patent is set forth in the appended claims.

We claim as our invention:
 1. A method for estimating a geolocation, themethod comprising: illuminating, with a platform, a camping cell andadjacent cells of the camping cell with a camping beam and each of theadjacent cells with adjacent beams; receiving a camping beam signalstrength, a camping beam center, adjacent beams signal strengths, and anassociated relative offset from the camping beam center for points inthe camping cell; profiling, at each point of the points, ratios of thecamping beam signal strength to each one of the adjacent beams signalstrengths; mapping the ratios and the associated relative offset of eachpoint of the points; and estimating a relative geolocation of a userterminal (UT) from the camping beam center based on a UT camping beamsignal strength and UT adjacent beams signal strengths, wherein themapping comprises pretraining neural network weights of a neural networkwith ratios and the associated relative offsets, and the estimatingestimates the relative geolocation of the UT with the neural network. 2.The method of claim 1, further comprising receiving a UT geolocation, aUT camping beam signal strength and UT adjacent beams signal strengths;and training the neural network with the UT geolocation, the UT campingbeam signal strength and UT adjacent beams signal strengths.
 3. Themethod of claim 1, wherein the receiving comprises measuring, at one ormore of the points, the camping beams signal strength and the adjacentbeams signal strengths.
 4. The method of claim 1, wherein the receivingcomprises computing, at one or more of the points, the camping beamssignal strength and the adjacent beams signal strengths.
 5. The methodof claim 1, further comprising adjusting the camping beam signalstrength and each one of the adjacent beams signal strengths to areference transmit power.
 6. The method of claim 1, wherein the ratiosare calculated as Pc/Pai or 10*log10(Pc/Pai), with the Pc set to thecamping beam signal strength and the Pai set to each one of the adjacentbeams signal strengths in turn.
 7. The method of claim 1, furthercomprising: predicting a location estimate offset based on a slowvarying Geosynchronous-Earth Orbit satellite pointing error or amovement of the camping beam center based on the relative geolocation,an actual geolocation of the UT and ephemeris of the platform; andcompensating for the location estimate offset in the estimating.
 8. Themethod of claim 1, wherein the neural network is updated duringoperation with a geolocation information for a pilot UT disposed in thecamping cell.
 9. A method for estimating a geolocation, the methodcomprising: illuminating, with a platform, a camping cell and adjacentcells of the camping cell with a camping beam and each of the adjacentcells with adjacent beams; receiving a camping beam signal strength, acamping beam center, adjacent beams signal strengths, and an associatedrelative offset from the camping beam center for points in the campingcell; profiling, at each point of the points, ratios of the camping beamsignal strength to each one of the adjacent beams signal strengths;mapping the ratios and the associated relative offset of each point ofthe points; and estimating a relative geolocation of a user terminal(UT) from the camping beam center based on a UT camping beam signalstrength and UT adjacent beams signal strengths, wherein the mappingcomprises populating a look up table (LUT) with ratios and theassociated relative offset, and the estimating by interpolation frommeasured ratios of the UT camping beam signal strength and the UTadjacent beams signal strengths between the ratios of points in the LUTto find a best match, and determining the relative geolocation of the UTbased on the best match.
 10. The method of claim 9, further comprisingreceiving a UT geolocation, a UT camping beam signal strength and UTadjacent beams signal strengths, wherein the populating comprises addingthe UT geolocation, the UT camping beam signal strength and UT adjacentbeams signal strengths to the LUT.
 11. The method of claim 9, whereinthe receiving comprises measuring, at one or more of the points, thecamping beams signal strength and the adjacent beams signal strengths.12. The method of claim 9, wherein the receiving comprises computing, atone or more of the points, the camping beams signal strength and theadjacent beams signal strengths.
 13. The method of claim 9, furthercomprising adjusting the camping beam signal strength and each one ofthe adjacent beams signal strengths to a reference transmit power. 14.The method of claim 9, wherein the ratios are calculated as Pc/Pai or10*log10(Pc/Pai), with the Pc set to the camping beam signal strengthand the Pai set to each one of the adjacent beams signal strengths inturn.
 15. The method of claim 9, further comprising: predicting alocation estimate offset based on a slow varying Geosynchronous-EarthOrbit satellite pointing error or a movement of the camping beam centerbased on the relative geolocation, an actual geolocation of the UT andephemeris of the platform; and compensating for the location estimateoffset in the estimating.
 16. A method for estimating a geolocation, themethod comprising: illuminating, with a platform, a camping cell andadjacent cells of the camping cell with a camping beam and each of theadjacent cells with adjacent beams; receiving a camping beam signalstrength, a camping beam center, adjacent beams signal strengths, and anassociated relative offset from the camping beam center for points inthe camping cell; profiling, at each point of the points, ratios of thecamping beam signal strength to each one of the adjacent beams signalstrengths; mapping the ratios and the associated relative offset of eachpoint of the points; estimating a relative geolocation of a userterminal (UT) from the camping beam center based on a UT camping beamsignal strength and UT adjacent beams signal strengths; predicting alocation estimate offset based on a slow varying Geosynchronous-EarthOrbit satellite pointing error or a movement of the camping beam centerbased on the relative geolocation, an actual geolocation of the UT andephemeris of the platform; and compensating for the location estimateoffset in the estimating.
 17. The method of claim 16, wherein theplatform is selected from a High-Altitude Platform (HAP), GeosynchronousEarth Orbit (GEO) satellite, a Medium Earth Orbit (MEO), a Low EarthOrbit (LEO) satellite, an airplane, or a platform 20,000 feet or abovesea-level.
 18. The method of claim 16, wherein the points are normalizedto a triple radius cross point.
 19. The method of claim 16, wherein thecamping beam signal strength, and the adjacent beams signal strengthsare simulated using a beam pattern in a far field.
 20. The method ofclaim 16, wherein a frequency of the compensating is based on a movementspeed of the platform relative to the camping beam center.